A Survey on Multiview 3D Motion Capture for Videos


A Survey on Multiview 3D Motion Capture for Videos – Recently there has been interest in 3D-based robotic control of objects, in particular, in cases where it is possible to detect and classify objects which are moving, but cannot be detected visually. The aim of this study is to train a 3D robotic arm to perform this task. The proposed method uses deep convolutional neural network (CNN) for object detection under unconstrained 3D vision. The network is trained by using a 3D camera with a pose and bounding box. The arm is equipped with articulated hand to assist in its tracking as it is tracked with a robot arm using the CNN architecture. The proposed method is evaluated on a data set with 1,848 objects and a 3D reconstruction of the object in question. Our proposed method is evaluated on a dataset with 1,251 objects and a 3D reconstruction of the object in question. Our method outperforms other approaches by orders of magnitude and achieves very high accuracy rates and comparable speed for training a 3D robotic arm to perform the pose recognition task.

Image classification is a challenging problem due to the wide variation of images used in many image processing applications. In each particular problem, researchers have to make use of various techniques such as supervised learning, multilevel learning, and machine learning. The problem is usually characterized by one of two major characteristics: a) image quality is highly variable, and b) it is difficult to estimate the image quality in terms of the true class labels. Therefore, a novel approach is to combine a supervised and a supervised image classification to gain a better and better classification performance. In this paper, we propose and evaluate an unsupervised Deep Reinforcement Learning (DRL) method which combines a supervised and a supervised image classification with a reinforcement learning (RL) method: (1) the RL method learns a model of an image, and (2) the RL method can learn a high-dimensional representation of the image with more accuracy than the supervised model, by training the RL model to classify it. We demonstrate our method on the ILSVRC 2017 and ILSVRC 2012 benchmark datasets.

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A Survey on Multiview 3D Motion Capture for Videos

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  • Bias-Aware Recommender System using Topic Modeling

    Towards a unified view on image quality assessmentImage classification is a challenging problem due to the wide variation of images used in many image processing applications. In each particular problem, researchers have to make use of various techniques such as supervised learning, multilevel learning, and machine learning. The problem is usually characterized by one of two major characteristics: a) image quality is highly variable, and b) it is difficult to estimate the image quality in terms of the true class labels. Therefore, a novel approach is to combine a supervised and a supervised image classification to gain a better and better classification performance. In this paper, we propose and evaluate an unsupervised Deep Reinforcement Learning (DRL) method which combines a supervised and a supervised image classification with a reinforcement learning (RL) method: (1) the RL method learns a model of an image, and (2) the RL method can learn a high-dimensional representation of the image with more accuracy than the supervised model, by training the RL model to classify it. We demonstrate our method on the ILSVRC 2017 and ILSVRC 2012 benchmark datasets.


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